63772c5cb80b0124b6722f705759ed0e49bb679c,mmdet/core/export/pytorch2onnx.py,,preprocess_example_input,#Any#,88

Before Change

        mean = np.array(normalize_cfg["mean"], dtype=np.float32)
        std = np.array(normalize_cfg["std"], dtype=np.float32)
        one_img = mmcv.imnormalize(one_img, mean, std)
    one_img = mmcv.imresize(one_img, input_shape[2:][::-1]).transpose(2, 0, 1)
    one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_(
        True)
    (_, C, H, W) = input_shape
    one_meta = {

After Change

    input_shape = input_config["input_shape"]
    one_img = mmcv.imread(input_path)
    one_img = mmcv.imresize(one_img, input_shape[2:][::-1])
    show_img = one_img.copy()
    if "normalize_cfg" in input_config.keys():
        normalize_cfg = input_config["normalize_cfg"]
        mean = np.array(normalize_cfg["mean"], dtype=np.float32)
        std = np.array(normalize_cfg["std"], dtype=np.float32)

Instances